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20182026
most citedLearning to Correlate in Multi-Player General-Sum Sequential Games

20 citations · 47 across the 43 of their papers we have counts for

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cs.GT2026

Online Resource Allocation With General Constraints

Eleonora Fidelia Chiefari, Francesco Emanuele Stradi, Matteo Castiglioni +1

Online resource allocation (ORA) is a fundamental framework for sequential decision-making problems under budget constraints, with applications ranging from online advertising to r…

cs.GT2026

Regret Minimization in Bilateral Trade With Perturbed Markets

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

We address the problem of maximizing Gain from Trade (GFT) in repeated buyer-seller exchanges subject to global budget balance constraints. While this problem is well-understood in…

cs.GT2026

A Stronger Benchmark for Online Bilateral Trade: From Fixed Prices to Distributions

Anna Lunghi, Mattia Piccinato, Matteo Castiglioni +1

We study online bilateral trade, where a learner facilitates repeated exchanges between a buyer and a seller to maximize the Gain From Trade (GFT), i.e., the social welfare. In doi…

cs.GT2026

Learning in Bayesian Stackelberg Games With Unknown Follower's Types

Matteo Bollini, Francesco Bacchiocchi, Samuel Coutts +2

We study online learning in Bayesian Stackelberg games, where a leader repeatedly interacts with a follower whose unknown private type is independently drawn at each round from an…

cs.GT2025

Better Regret Rates in Bilateral Trade via Sublinear Budget Violation

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

Bilateral trade is a central problem in algorithmic economics, and recent work has explored how to design trading mechanisms using no-regret learning algorithms. However, no-regret…

cs.GT2025

Online Two-Sided Markets: Many Buyers Enhance Learning

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

We study a repeated trading problem in which a mechanism designer facilitates trade between a single seller and multiple buyers. Our model generalizes the classic bilateral trade s…